# Load the pandas libraryimport pandas as pd# Load numpy for array manipulationimport numpy as np# Load seaborn plotting libraryimport seaborn as snsimport matplotlib.pyplot as plt# Set font sizes in plotssns.set(font_scale =2)# Display all columnspd.set_option('display.max_columns', None)# Import Advertising dataAdvertising = pd.read_csv("../data/Advertising.csv", index_col =0)Advertising
# Visualization by pair plotsns.pairplot(data = Advertising)
A few questions we might ask
Is there a relationship between advertising budget and sales?
How strong is the relationship between advertising budget and sales?
Which media contribute to sales?
How accurately can we predict future sales?
Is the relationship linear?
Is there synergy among the advertising media?
3 Simple linear regression vs multiple linear regression
In simple linear regression, we assume a model \[
Y = \underbrace{\beta_0}_{\text{intercept}} + \underbrace{\beta_1}_{\text{slope}} X + \epsilon.
\] For example \[
\text{sale} \approx \beta_0 + \beta_1 \times \text{TV}.
\] The lower left plot in the pair plot (R) displays the fitted line \[
\hat y = \hat \beta_0 + \hat \beta_1 \times \text{TV}.
\]
In multiple linear regression, we assume the model \[
Y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \cdots + \beta_p X_p + \epsilon.
\] For example, \[
\text{sales} \approx \beta_0 + \beta_1 \times \text{TV} + \beta_2 \times \text{radio} + \cdots + \beta_3 \times \text{newspaper}.
\] We review the multiple linear regression below.
4 Estimation in multiple linear regression
We estimate regression coefficients \(\beta_0, \beta_1, \ldots, \beta_p\) by the method of least squares, which minimizes the sum of squared residuals \[
\text{RSS} = \sum_{i=1}^n (y_i - \hat y_i)^2 = \sum_{i=1}^n (y_i - \beta_0 - \beta_1 x_{i1} - \cdots - \beta_p x_{ip})^2.
\]
The minimizer \(\hat \beta_0, \hat \beta_1, \ldots, \hat \beta_p\) admits the analytical solution \[
\hat \beta = (\mathbf{X}^T \mathbf{X})^{-1} \mathbf{X}^T \mathbf{y}.
\] and we can make prediction using the formula \[
\hat y = \hat \beta_0 + \hat \beta_1 x_1 + \cdots + \hat \beta_p x_p.
\]
# sklearn does not offer much besides coefficient estimatefrom sklearn.linear_model import LinearRegressionX = Advertising[['TV', 'radio', 'newspaper']]y = Advertising['sales']lmod = LinearRegression().fit(X, y)lmod.intercept_
np.float64(2.9388893694594085)
lmod.coef_
array([ 0.04576465, 0.18853002, -0.00103749])
# Score is the R^2lmod.score(X, y)
0.8972106381789522
import statsmodels.api as smimport statsmodels.formula.api as smf# Fit linear regressionlmod = smf.ols(formula ='sales ~ TV + radio + newspaper', data = Advertising).fit()lmod.summary()
OLS Regression Results
Dep. Variable:
sales
R-squared:
0.897
Model:
OLS
Adj. R-squared:
0.896
Method:
Least Squares
F-statistic:
570.3
Date:
Mon, 22 Dec 2025
Prob (F-statistic):
1.58e-96
Time:
23:19:18
Log-Likelihood:
-386.18
No. Observations:
200
AIC:
780.4
Df Residuals:
196
BIC:
793.6
Df Model:
3
Covariance Type:
nonrobust
coef
std err
t
P>|t|
[0.025
0.975]
Intercept
2.9389
0.312
9.422
0.000
2.324
3.554
TV
0.0458
0.001
32.809
0.000
0.043
0.049
radio
0.1885
0.009
21.893
0.000
0.172
0.206
newspaper
-0.0010
0.006
-0.177
0.860
-0.013
0.011
Omnibus:
60.414
Durbin-Watson:
2.084
Prob(Omnibus):
0.000
Jarque-Bera (JB):
151.241
Skew:
-1.327
Prob(JB):
1.44e-33
Kurtosis:
6.332
Cond. No.
454.
Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
5 Interpreting regression coefficients
We interpret \(\beta_j\) as the average effect on \(Y\) of a a one-unit increase in \(X_j\), holding all other predictors fixed.
a regression coefficient \(\beta_j\) estimates the expected change in \(Y\) per unit change in \(X_j\), with all other predictors held fixed. But predictors usually change together!
“Data Analysis and Regression”, Mosteller and Tukey 1977
Only when the predictors are uncorrelated (balanced or orthogonal design), each coefficient can be estimated, interpreted, and tested separately.
In other words, only in a balanced design, the regression coefficients estimated from multiple linear regression are the same as those from separate simple linear regressions.
Claims of causality should be avoided for observational data.
6 Some important questions
6.1 Goodness of fit
Question: Is at least one of the predictors \(X_1, X_2, \ldots, X_p\) useful in predicting the response?
We can use the F-statistic to assess the fit of the overall model. Formally we are testing the null hypothesis \[
H_0: \beta_1 = \cdots = \beta_p = 0
\] versus the alternative \[
H_a: \text{at least one } \beta_j \text{ is non-zero}.
\] F-statistic is defined as \[
F = \frac{(\text{TSS} - \text{RSS})/p}{\text{RSS}/(n-p-1)} \sim F_{p, n-p-1},
\] where \[
\text{TSS} = \sum_{i=1}^n (y_i - \bar y)^2.
\] If the linear model assumptions are correct, one can show that \[
\mathbf{E}{\text{RSS}/(n − p − 1)} = \sigma^2
\] and that, provided \(H_0\) is true, \[
\mathbf{E}{(\text{TSS} − \text{RSS})/p} = \sigma^2.
\] Hence, when there is no relationship between the response and predictors, F-statistic to take on a value close to 1. On the other hand, if \(H_a\) is true, then \(\mathbf{E}{(\text{TSS} − \text{RSS})/p} > \sigma^2\), so we expect F to be greater than 1.
The p-value for the F-statistic is computed using the F-distribution with \(p\) and \(n-p-1\) degrees of freedom.
In general, the F-statistic for testing \[
H_0: \text{ a restricted model } \omega
\] versus \[
H_a: \text{a more general model } \Omega
\] takes the form \[
F = \frac{(\text{RSS}_\omega - \text{RSS}_\Omega) / (\text{df}(\Omega) - \text{df}(\omega))}{\text{RSS}_\Omega / (n - \text{df}(\Omega))},
\] where df is the degree of freedom (roughly speaking the number of free parameters) of a model.
the p-value associated with the F -statistic is essentially zero, so we have extremely strong evidence that at least one of the media is associated with increased sales.
6.2 Model selection
Question: Do all the predictors help to explain \(Y\) , or is only a subset of the predictors useful?
For a naive implementation of the best subset, forward selection, and backward selection regressions in Python, interesting readers can read this blog.
6.2.1 Best subset regression
The most direct approach is called all subsets or best subsets regression: we compute the least squares fit for all possible subsets and then choose between them based on some criterion that balances training error with model size.
from abess.linear import LinearRegressionfrom patsy import dmatrices# Create design matrix and response vector using R-like formulaym, Xm = dmatrices('sales ~ TV + radio + newspaper', data = Advertising, return_type ='matrix' )lmod_abess = LinearRegression(support_size =range(3))lmod_abess.fit(Xm, ym)lmod_abess.coef_
However we often can’t examine all possible models, since there are \(2^p\) of them. For example, when \(p = 30\), there are \(2^{30} = 1,073,741,824\) models!
Recent advances make possible to solve best subset regression with \(p \sim 10^3 \sim 10^5\) with high-probability guarantee of optimality. See this article.
For really big \(p\), we need some heuristic approaches to search through a subset of them. We discuss three commonly use approaches next.
6.2.2 Forward selection
Begin with the null model, a model that contains an intercept but no predictors.
Fit \(p\) simple linear regressions and add to the null model the variable that results in the lowest RSS (equivalently the lowest AIC).
Add to that model the variable that results in the lowest RSS among all two-variable models.
Continue until some stopping rule is satisfied, for example when the AIC does not decrease anymore.
step(lmod_null,scope =list(lower = sales ~1, upper = sales ~ TV + radio + newspaper),trace =TRUE, direction ="forward" )
Start: AIC=661.8
sales ~ 1
Df Sum of Sq RSS AIC
+ TV 1 3314.6 2102.5 474.52
+ radio 1 1798.7 3618.5 583.10
+ newspaper 1 282.3 5134.8 653.10
<none> 5417.1 661.80
Step: AIC=474.52
sales ~ TV
Df Sum of Sq RSS AIC
+ radio 1 1545.62 556.91 210.82
+ newspaper 1 183.97 1918.56 458.20
<none> 2102.53 474.52
Step: AIC=210.82
sales ~ TV + radio
Df Sum of Sq RSS AIC
<none> 556.91 210.82
+ newspaper 1 0.088717 556.83 212.79
Call:
lm(formula = sales ~ TV + radio, data = Advertising)
Coefficients:
(Intercept) TV radio
2.92110 0.04575 0.18799
6.2.3 Backward selection
Start with all variables in the model.
Remove the variable with the largest \(p\)-value. That is the variable that is the least statistically significant.
The new \((p-1)\)-variable model is fit, and the variable with the largest p-value is removed.
Continue until a stopping rule is reached. For instance, we may stop when AIC does not decrease anymore, or when all remaining variables have a significant \(p\)-value defined by some significance threshold.
Backward selection cannot start with a model with \(p > n\).
step(lmod,scope =list(lower = sales ~1, upper = sales ~ TV + radio + newspaper),trace =TRUE, direction ="backward" )
Start: AIC=212.79
sales ~ TV + radio + newspaper
Df Sum of Sq RSS AIC
- newspaper 1 0.09 556.9 210.82
<none> 556.8 212.79
- radio 1 1361.74 1918.6 458.20
- TV 1 3058.01 3614.8 584.90
Step: AIC=210.82
sales ~ TV + radio
Df Sum of Sq RSS AIC
<none> 556.9 210.82
- radio 1 1545.6 2102.5 474.52
- TV 1 3061.6 3618.5 583.10
Call:
lm(formula = sales ~ TV + radio, data = Advertising)
Coefficients:
(Intercept) TV radio
2.92110 0.04575 0.18799
6.2.4 Mixed selection
We alternately perform forward and backward steps until all variables in the model have a sufficiently low \(p\)-value, and all variables outside the model would have a large \(p\)-value if added to the model.
step(lmod_null,scope =list(lower = sale ~1, upper = sale ~ TV + radio + newspaper),trace =TRUE, direction ="both" )
Start: AIC=661.8
sales ~ 1
Df Sum of Sq RSS AIC
+ TV 1 3314.6 2102.5 474.52
+ radio 1 1798.7 3618.5 583.10
+ newspaper 1 282.3 5134.8 653.10
<none> 5417.1 661.80
Step: AIC=474.52
sales ~ TV
Df Sum of Sq RSS AIC
+ radio 1 1545.6 556.9 210.82
+ newspaper 1 184.0 1918.6 458.20
<none> 2102.5 474.52
- TV 1 3314.6 5417.1 661.80
Step: AIC=210.82
sales ~ TV + radio
Df Sum of Sq RSS AIC
<none> 556.9 210.82
+ newspaper 1 0.09 556.8 212.79
- radio 1 1545.62 2102.5 474.52
- TV 1 3061.57 3618.5 583.10
Call:
lm(formula = sales ~ TV + radio, data = Advertising)
Coefficients:
(Intercept) TV radio
2.92110 0.04575 0.18799
6.2.5 Other model selection criteria
Commonly used model selection criteria include Mallow’s \(C_p\), Akaike information criterion (AIC), Bayesian information criterion (BIC), adjusted \(R^2\), and cross-validation (CV).
6.3 Model fit
Question: How well does the selected model fit the data?
\(R^2\): the fraction of variance explained \[
R^2 = 1 - \frac{\text{RSS}}{\text{TSS}} = 1 - \frac{\sum_i (y_i - \hat y_i)^2}{\sum_i (y_i - \bar y)^2}.
\] It turns out (HW1 bonus question) \[
R^2 = \operatorname{Cor}(Y, \hat Y)^2.
\] Adding more predictors into a model always increase \(R^2\).
From computer output, we see the fitted linear model by using TV, radio, and newspaper has an \(R^2=0.8972\).
The adjusted \(R_a^2\) adjusts to the fact that a larger model also uses more parameters. \[
R_a^2 = 1 - \frac{\text{RSS}/(n-p-1)}{\text{TSS}/(n-1)}.
\] Adding a predictor will only increase \(R_a^2\) if it has some predictive value.
The rooted squared error (RSE)\[
\text{RSE} = \sqrt{\frac{\text{RSS}}{n - p - 1}}
\] is an estimator of the noise standard error \(\sqrt{\operatorname{Var}(\epsilon)}\). The smaller RSE, the better fit of the model.
6.4 Prediction
Question: Given a set of predictor values, what response value should we predict, and how accurate is our prediction?
Given a new set of predictors \(x\), we predict \(y\) by \[
\hat y = \hat \beta_0 + \hat \beta_1 x_1 + \cdots + \hat \beta_p x_p.
\]
Under the selected model \[
\text{sales} \approx \beta_0 + \beta_1 \times \text{TV} + \beta_2 \times \text{radio},
\] how many units of sale do we expect with $100k budget in TV and $20k budget in radio?
95% Confidence interval: probability of covering the true \(f(X)\) is 0.95.
95% Predictive interval: probability of covering the true \(Y\) is 0.95.
Predictive interval is always wider than the confidence interval because it incorporates the randomness in both \(\hat \beta\) and \(\epsilon\).
7 Qualitative (or categorical) predictors
In the Advertising data, all predictors are quantitative (or continuous).
The Credit data has a mix of quantitative (Balance, Age, Cards, Education, Income, Limit, Rating) and qualitative predictors (Own, Student, Married, Region).
balance: average credit card debt for each individual
quantitative predictors: age, cards (number of credit cards), education (years of education), income (in thousands of dollars), limit (credit limit), and rating (credit rating)
qualitative variables: own (house ownership), student (student status), status (marital status), and region (East, West or South).
# A tibble: 400 × 11
Income Limit Rating Cards Age Education Own Student Married Region
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <fct> <fct> <fct>
1 14.9 3606 283 2 34 11 No No Yes South
2 106. 6645 483 3 82 15 Yes Yes Yes West
3 105. 7075 514 4 71 11 No No No West
4 149. 9504 681 3 36 11 Yes No No West
5 55.9 4897 357 2 68 16 No No Yes South
6 80.2 8047 569 4 77 10 No No No South
7 21.0 3388 259 2 37 12 Yes No No East
8 71.4 7114 512 2 87 9 No No No West
9 15.1 3300 266 5 66 13 Yes No No South
10 71.1 6819 491 3 41 19 Yes Yes Yes East
Balance
<dbl>
1 333
2 903
3 580
4 964
5 331
6 1151
7 203
8 872
9 279
10 1350
# ℹ 390 more rows
Income Limit Rating Cards Age Education Own Student Married \
0 14.891 3606 283 2 34 11 No No Yes
1 106.025 6645 483 3 82 15 Yes Yes Yes
2 104.593 7075 514 4 71 11 No No No
3 148.924 9504 681 3 36 11 Yes No No
4 55.882 4897 357 2 68 16 No No Yes
.. ... ... ... ... ... ... ... ... ...
395 12.096 4100 307 3 32 13 No No Yes
396 13.364 3838 296 5 65 17 No No No
397 57.872 4171 321 5 67 12 Yes No Yes
398 37.728 2525 192 1 44 13 No No Yes
399 18.701 5524 415 5 64 7 Yes No No
Region Balance
0 South 333
1 West 903
2 West 580
3 West 964
4 South 331
.. ... ...
395 South 560
396 East 480
397 South 138
398 South 0
399 West 966
[400 rows x 11 columns]
# Visualization by pair plotsns.pairplot(data = Credit)
By default, most software create dummy variables, also called one-hot coding, for categorical variables. Both Python (statsmodels) and R use the first level (in alphabetical order) as the base level.
For example: own has two levels: no and yes. The dummy variable own is 1 if own is yes and 0 otherwise: \[
x_i = \begin{cases}
1 & \text{if own}_i = \text{yes, } \text{i.e., if ith person owns a house} \\
0 & \text{if own}_i = \text{no, } \text{i.e., if ith person does not own a house}
\end{cases}
\] This results in the model \[
y_i = \beta_0 + \beta_1x_i + \epsilon = \begin{cases}
\beta_0 + \beta_1 + \epsilon & \text{if own}_i = \text{yes, } \text{i.e., if ith person owns a house} \\
\beta_0 + \epsilon & \text{if own}_i = \text{no, } \text{i.e., if ith person does not own a house}
\end{cases}
\]
\(\beta_0\): the average credit card balance among those who do not own
\(\beta_0 + \beta_1\): the average credit card balance among those who do own their house
\(\beta_1\): the average difference in credit card balance between owners and non-owners
Since the p-value for the dummy variable is very high, this indicates that there is no statistical evidence of a difference in average credit card balance based on house ownership.
lm(Balance ~ ., data = Credit) %>%tbl_regression() %>%bold_labels() %>%bold_p(t =0.05)
Characteristic
Beta
95% CI
p-value
Income
-7.8
-8.3, -7.3
<0.001
Limit
0.19
0.13, 0.26
<0.001
Rating
1.1
0.17, 2.1
0.021
Cards
18
9.2, 26
<0.001
Age
-0.61
-1.2, -0.04
0.037
Education
-1.1
-4.2, 2.0
0.5
Own
No
—
—
Yes
-11
-30, 8.8
0.3
Student
No
—
—
Yes
426
393, 459
<0.001
Married
No
—
—
Yes
-8.5
-29, 12
0.4
Region
East
—
—
South
10
-14, 34
0.4
West
17
-11, 45
0.2
Abbreviation: CI = Confidence Interval
# Hack to get all predictorsmy_formula ="Balance ~ "+"+".join(Credit.columns.difference(["Balance"]))# Create design matrix and response vector using R-like formulay, X = dmatrices( my_formula, data = Credit, return_type ='dataframe' )# One-hot coding for categorical variables X
# Fit linear regressionsm.OLS(y, X).fit().summary()
OLS Regression Results
Dep. Variable:
Balance
R-squared:
0.955
Model:
OLS
Adj. R-squared:
0.954
Method:
Least Squares
F-statistic:
750.3
Date:
Mon, 22 Dec 2025
Prob (F-statistic):
1.11e-253
Time:
23:19:27
Log-Likelihood:
-2398.7
No. Observations:
400
AIC:
4821.
Df Residuals:
388
BIC:
4869.
Df Model:
11
Covariance Type:
nonrobust
coef
std err
t
P>|t|
[0.025
0.975]
Intercept
-479.2079
35.774
-13.395
0.000
-549.543
-408.873
Married[T.Yes]
-8.5339
10.363
-0.824
0.411
-28.908
11.841
Own[T.Yes]
-10.6532
9.914
-1.075
0.283
-30.145
8.839
Region[T.South]
10.1070
12.210
0.828
0.408
-13.899
34.113
Region[T.West]
16.8042
14.119
1.190
0.235
-10.955
44.564
Student[T.Yes]
425.7474
16.723
25.459
0.000
392.869
458.626
Age
-0.6139
0.294
-2.088
0.037
-1.192
-0.036
Cards
17.7245
4.341
4.083
0.000
9.190
26.259
Education
-1.0989
1.598
-0.688
0.492
-4.241
2.043
Income
-7.8031
0.234
-33.314
0.000
-8.264
-7.343
Limit
0.1909
0.033
5.824
0.000
0.126
0.255
Rating
1.1365
0.491
2.315
0.021
0.171
2.102
Omnibus:
34.899
Durbin-Watson:
1.968
Prob(Omnibus):
0.000
Jarque-Bera (JB):
41.766
Skew:
0.782
Prob(JB):
8.52e-10
Kurtosis:
3.241
Cond. No.
3.87e+04
Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 3.87e+04. This might indicate that there are strong multicollinearity or other numerical problems.
There are many different ways of coding qualitative variables, measuring particular contrasts.
For example, instead of 0/1 coding scheme, we could create a dummy variable as: own is 1 if own is yes and -1 otherwise: \[
x_i = \begin{cases}
1 & \text{if own}_i = \text{yes, } \text{i.e., if ith person owns a house} \\
-1 & \text{if own}_i = \text{no, } \text{i.e., if ith person does not own a house}
\end{cases}
\] This results in the model \[
y_i = \beta_0 + \beta_1x_i + \epsilon = \begin{cases}
\beta_0 + \beta_1 + \epsilon & \text{if own}_i = \text{yes, } \text{i.e., if ith person owns a house} \\
\beta_0 -\beta_1 \epsilon & \text{if own}_i = \text{no, } \text{i.e., if ith person does not own a house}
\end{cases}
\]
Question: how to interpret \(\beta_0\) and \(\beta_1\)?
7.1 More than two levels
When a qualitative predictor has more than two levels, more dummy variables is needed to represent all possible values.
For example, for the region variable we create two dummy variables: \[
x_{i1} = \begin{cases}
1 & \text{ if ith person is from South} \\
0 & \text{ if ith person is not from South}
\end{cases}
\] and \[
x_{i2} = \begin{cases}
1 & \text{ if ith person is from West} \\
0 & \text{ if ith person is not from West}
\end{cases}
\] This results in \[
y_i = \beta_0 + \beta_1x_{i1} + \beta_2x_{i2} + \epsilon =
\begin{cases}
\beta_0 + \beta_1 + \epsilon & \text{ if ith person is from South} \\
\beta_0 + \beta_2 + \epsilon & \text{ if ith person is from West}\\
\beta_0 + \epsilon & \text{ if ith person is from East}
\end{cases}
\]
Question: how to interpret \(\beta_0\), \(\beta_1\), and \(\beta_2\)?
There will always be one fewer dummy variable than the number of levels.
The level with no dummy variable (i.e., East) is known as the baseline or reference level.
The choice of reference level depends on the question. It doesn’t impact the prediction. But it will impact the value of coefficients and their p-values.
Use \(F\)-test to test \(\beta_1=\beta_2=0\)
8 Interactions
In our previous analysis of the Advertising data, we assumed that the effect on sales of increasing one advertising medium is independent of the amount spent on the other media \[
\text{sales} \approx \beta_0 + \beta_1 \times \text{TV} + \beta_2 \times \text{radio}.
\]
But suppose that spending money on radio advertising actually increases the effectiveness of TV advertising, so that the slope term for TV should increase as radio increases.
In this situation, given a fixed budget of $100,000, spending half on radio and half on TV may increase sales more than allocating the entire amount to either TV or to radio.
In marketing, this is known as a synergy effect, and in statistics it is referred to as an interaction effect.
# Design matrix with interactiony, X_int = dmatrices('sales ~ 1 + TV * radio', data = Advertising, return_type ='dataframe' )# Fit linear regression with interactionsm.OLS(y, X_int).fit().summary()
OLS Regression Results
Dep. Variable:
sales
R-squared:
0.968
Model:
OLS
Adj. R-squared:
0.967
Method:
Least Squares
F-statistic:
1963.
Date:
Mon, 22 Dec 2025
Prob (F-statistic):
6.68e-146
Time:
23:19:28
Log-Likelihood:
-270.14
No. Observations:
200
AIC:
548.3
Df Residuals:
196
BIC:
561.5
Df Model:
3
Covariance Type:
nonrobust
coef
std err
t
P>|t|
[0.025
0.975]
Intercept
6.7502
0.248
27.233
0.000
6.261
7.239
TV
0.0191
0.002
12.699
0.000
0.016
0.022
radio
0.0289
0.009
3.241
0.001
0.011
0.046
TV:radio
0.0011
5.24e-05
20.727
0.000
0.001
0.001
Omnibus:
128.132
Durbin-Watson:
2.224
Prob(Omnibus):
0.000
Jarque-Bera (JB):
1183.719
Skew:
-2.323
Prob(JB):
9.09e-258
Kurtosis:
13.975
Cond. No.
1.80e+04
Notes: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 1.8e+04. This might indicate that there are strong multicollinearity or other numerical problems.
The results in this table suggests that the TV \(\times\) Radio interaction is important.
The \(R^2\) for the interaction model is 96.8%, compared to only 89.7% for the model that predicts sales using TV and radio without an interaction term.
This means that (96.8 - 89.7)/(100 - 89.7) = 69% of the variability in sales that remains after fitting the additive model has been explained by the interaction term.
The coefficient estimates in the table suggest that an increase in TV advertising of $1,000 is associated with increased sales of \[
(\hat \beta_1 + \hat \beta_3 \times \text{radio}) \times 1000 = 19 + 1.1 \times \text{radio units}.
\]
An increase in radio advertising of $1,000 will be associated with an increase in sales of \[
(\hat \beta_2 + \hat \beta_3 \times \text{TV}) \times 1000 = 29 + 1.1 \times \text{TV units}.
\]
Sometimes it is the case that an interaction term has a very small p-value, but the associated main effects (in this case, TV and radio) do not.
The hierarchy principle staties
If we include an interaction in a model, we should also include the main effects, even if the \(p\)-values associated with their coefficients are not significant.
The rationale for this principle is that interactions are hard to interpret in a model without main effects – their meaning is changed. Specifically, the interaction terms still contain main effects, if the model has no main effect terms.
8.1 Interaction between a qualitative variable and a quantitative variable
Consider the Credit data set, let’s predict balance using the income (quantitative) and student (qualitative) variables.
In the absence of an interaction term, the model takes the form \[\begin{align*}
\text{balance}_i &\approx \beta_0 + \beta_1 \times \text{income}_i +
\begin{cases}
\beta_2 & \text{ if ith person is a student}\\
0 & \text{ if ith person is not a student}
\end{cases}\\
&= \beta_1 \times \text{income}_i +
\begin{cases}
\beta_0+\beta_2 & \text{ if ith person is a student}\\
\beta_0 & \text{ if ith person is not a student}
\end{cases}
\end{align*}\]
With an interaction term, i.e., a change in income may have a very different effect on the credit card balance of a student versus a non-student. \[\begin{align*}
\text{balance}_i &\approx \beta_0 + \beta_1 \times \text{income}_i +
\begin{cases}
\beta_2 + \beta_3 \times \text{income}_i& \text{ if ith person is a student}\\
0 & \text{ if ith person is not a student}
\end{cases}\\
&=
\begin{cases}
\beta_0+\beta_2 + (\beta_1 + \beta_3) \times \text{income}_i& \text{ if ith person is a student}\\
\beta_0 + \beta_1 \times \text{income}_i& \text{ if ith person is not a student}
\end{cases}
\end{align*}\]
9 Potential Problems
9.1 Non-linear effects of predictors
Assumption for the linear regression model: there is a straight-line relationship between the predictors and the response. What if the data violates this assumptions?
ggplot(data = Auto, mapping =aes(x = horsepower, y = mpg)) +geom_point() +geom_smooth(method = lm, color =1) +geom_smooth(method = lm, formula = y ~poly(x, 2), color =2) +geom_smooth(method = lm, formula = y ~poly(x, 5), color =3) +labs(x ="Horsepower", y ="Miles per gallon")
The figure suggests that mpg vs horsepower to have a quadratic shape. And the following model \[
\text{mpg} = \beta_0 + \beta_1 \times \text{horsepower} + \beta_2 \times \text{horsepower}^2 + \epsilon
\] may provide a better fit. But it is still a linear model! So we can use standard linear regression software to estimate \(\beta_0\), \(\beta_1\), \(\beta_2\) in order to produce a non-linear fit.
plt.clf()# Linear fitax = sns.regplot( data = Auto, x ='horsepower', y ='mpg', order =1 )# Quadratic fit sns.regplot( data = Auto, x ='horsepower', y ='mpg', order =2, ax = ax )# 5-degree fit sns.regplot( data = Auto, x ='horsepower', y ='mpg', order =5, ax = ax )ax.set( xlabel ='Horsepower', ylabel ='Miles per gallon')
9.1.1 Residual Plots
A useful graphical tool for identifying non-linearity, outliers, etc.
Recall residuals are \[
\epsilon_i = y_i - \hat{y}_i
\] Predicted/fitted values are \[
\hat{y}_i = \mathbf{X}\hat{\beta}
\]
We plot the residuals versus the predicted (or fitted) values
# Linear fitlm.fit <-lm(mpg ~ horsepower, data = Auto) plot(lm.fit, which =c(1))
Ideally, the residual plot will show no discernible pattern. The presence of a pattern may indicate a problem with some aspect of the linear model.
# Linear fitlm.df2.fit <-lm(mpg ~poly(horsepower, 2), data = Auto) plot(lm.df2.fit, which =c(1))
If the residual plot indicates that there are non-linear associations in the data, then a simple approach is to use non-linear transformations of the predictors, such as \(\text{log}X\), \(\sqrt{X}\), and \(X^2\), in the regression model.
9.2 Correlation of error terms.
One important assumption of the linear regression model is that the error terms, \(\epsilon_1, \ldots, \epsilon_n\), are uncorrelated, e.g., longitudinal data, time series data, etc.
9.3 Non-constant variance of error terms.
Another important assumption of the linear regression model is that the error terms have a constant variance, \(\text{Var}(\epsilon_i) = \sigma^2\).
Residual plot
Transformation of outcome or weighted least squares.
9.4 Outliers.
9.5 High leverage points.
9.6 Collinearity.
Collinearity refers to the situation in which two or more predictor variables are closely related to one another.
library(ggplot2)ggplot(Credit, aes(x = Limit, y = Age)) +# Set up canvas with outcome variable on y-axisgeom_point( color ="red") +scale_x_continuous(breaks =seq(2000, 12000, by =2000)) +# Set x-axis limitsscale_y_continuous(breaks =seq(30, 80, by =10)) +xlab("Limit") +ylab("Age") +theme_bw() +theme(panel.background =element_rect(colour ="black", linewidth =1),panel.grid.major =element_blank(), panel.grid.minor =element_blank(), axis.line =element_line(colour ="black"))
ggplot(Credit, aes(x = Limit, y = Rating)) +# Set up canvas with outcome variable on y-axisgeom_point( color ="red") +scale_x_continuous(breaks =seq(2000, 12000, by =2000)) +# Set x-axis limitsscale_y_continuous(breaks =seq(200, 800, by =200)) +xlab("Limit") +ylab("Rating") +theme_bw() +theme(panel.background =element_rect(colour ="black", linewidth =1),panel.grid.major =element_blank(), panel.grid.minor =element_blank(), axis.line =element_line(colour ="black"))
Collinearity reduces the accuracy of the estimates of the regression coefficients, it causes the standard error to grow.
The power of the hypothesis test—the probability of correctly detecting a non-zero coefficient—is reduced by collinearity.
Solution:
drop one of the problematic variables from the regression.
combine the collinear variables together into a single predictor.
10 Generalizations of linear models
In much of the rest of this course, we discuss methods that expand the scope of linear models and how they are fit.
Classification problems: logistic regression, discriminant analysis, support vector machines.
